Atypia detected during breast screening and subsequent development of cancer: observational analysis of the Sloane atypia prospective cohort in England

Abstract Objective To explore how the number and type of breast cancers developed after screen detected atypia compare with the anticipated 11.3 cancers detected per 1000 women screened within one three year screening round in the United Kingdom. Design Observational analysis of the Sloane atypia prospective cohort in England. Setting Atypia diagnoses through the English NHS breast screening programme reported to the Sloane cohort study. This cohort is linked to the English Cancer Registry and the Mortality and Birth Information System for information on subsequent breast cancer and mortality. Participants 3238 women diagnosed as having epithelial atypia between 1 April 2003 and 30 June 2018. Main outcome measures Number and type of invasive breast cancers detected at one, three, and six years after atypia diagnosis by atypia type, age, and year of diagnosis. Results There was a fourfold increase in detection of atypia after the introduction of digital mammography between 2010 (n=119) and 2015 (n=502). During 19 088 person years of follow-up after atypia diagnosis (until December 2018), 141 women developed breast cancer. Cumulative incidence of cancer per 1000 women with atypia was 0.95 (95% confidence interval 0.28 to 2.69), 14.2 (10.3 to 19.1), and 45.0 (36.3 to 55.1) at one, three, and six years after atypia diagnosis, respectively. Women with atypia detected more recently have lower rates of subsequent cancers detected within three years (6.0 invasive cancers per 1000 women (95% confidence interval 3.1 to 10.9) in 2013-18 v 24.3 (13.7 to 40.1) in 2003-07, and 24.6 (14.9 to 38.3) in 2008-12). Grade, size, and nodal involvement of subsequent invasive cancers were similar to those of cancers detected in the general screening population, with equal numbers of ipsilateral and contralateral cancers. Conclusions Many atypia could represent risk factors rather than precursors of invasive cancer requiring surgery in the short term. Women with atypia detected more recently have lower rates of subsequent cancers detected, which might be associated with changes to mammography and biopsy techniques identifying forms of atypia that are more likely to represent overdiagnosis. Annual mammography in the short term after atypia diagnosis might not be beneficial. More evidence is needed about longer term risks.


Characteristics of women and their atypia in the Sloane atypia cohort
Table S1 Descriptive statistics of all women with atypia for the study period April 2003 to June 2018 Table S2 Number of women with atypia and characteristics of subsequent cancers detected following atypia diagnosis by atypia type for three time periods separately Figure S2 Proportion of atypia with microcalcifications present by year

Subsequent events following atypia
Table S3 Characteristics of subsequent invasive and non-invasive cancers by atypia type Table S4 Distribution of grade for subsequent invasive cancers compared to published figures Table S5 Location of 22 ipsilateral invasive cancers 3 years post atypia diagnosis by atypia type

Figure S3
Proportion of diagnostic management options performed by year Table S6 Invasive cancers per 1000 women with atypia at 1 year and 3 years post atypia diagnosis by management strategy and site of invasive cancers separately for atypia types

Cancers at 3-and 6-years post atypia and long-term risk
Table S8 Invasive cancer rates per 1000 women with atypia at 1 year, 3 years and 6 years post atypia diagnosis by age group Figure S4.Cumulative incidence function for invasive cancer and death from the main model, evaluated at 1 year, 3 years, and 6 years since diagnosis with atypia; by age at diagnosis, year of diagnosis and women's breast density (background parenchymal) (high figure 3a, low figure 3b).Table S9 Invasive cancer rates at 3 years post atypia by atypia type Table S10 Invasive cancer rates at 1 year, 3 years and 6 years post atypia diagnosis per 1000 women for 3 time periods under different scenarios Table S11 Invasive cancer rates at 1 year, 3 years and 6 years post atypia diagnosis per 1000 women for 3 time periods excluding women with FEA Table S12 Invasive cancer rates at 1 year, 3 years and 6 years post atypia diagnosis per 1000 women for 3 time periods for consecutive cases only

Modelling of cancer rates using the cause specific hazard method
Table S13 Model selection for models with causes of outcome: invasive cancer and death Table S14 Cancer rates (fitted values) at 1, 3, and 6 years since atypia diagnosis from main model (by age, year of diagnosis and breast background parenchymal density).Cumulative incidence of invasive cancer per 1000 women Table S15 Comparison of the risk of subsequent invasive cancers considering different factors Figure S6 Cause-specific hazard function for each cause of outcome from the main model by time since atypia diagnosis.Figure S7 Stacked cumulative incidence plots from the main model.Figure S8 Cause-specific hazards (a, b), stacked cumulative incidence functions (c, d) and cumulative incidence functions (e, f) evaluated at 1 year, 3 years, and 6 years since diagnosis with atypia, from the main model with death and invasive cancer split into ipsilateral and contralateral.Table S16 Fitted values at 1, 3, and 6 years since atypia diagnosis from main model with invasive cancer and DCIS combined.Figure S9 Cumulative incidence functions evaluated at 1 year, 3 years, and 6 years since diagnosis with atypia, from the main model with invasive cancer or DCIS combined for high (a) and low (b) background parenchymal density

Modelling of cancer rates using the subdistribution method
Table S17 Cancer rates (fitted values) at 1, 3, and 6 years since atypia diagnosis from main model, using the subdistribution method.Cumulative incidence of invasive cancer per 1000 women Figure S10 Cumulative incidence function for outcomes of both causes from the main model using the subdistribution method, evaluated at 1 year, 3 years, and 6 years since Recurrence/Further event data

Atypical Ductal Lesions
For this study we combined AIDEP and ADH in the ADH group.This decision was based on: 1) AIDEP was introduced as a term on the Sloane data collection form in 2019 following the UK national guidelines that the specific entity of ADH should not be diagnosed on standard core biopsy or diagnostic VAB.Therefore, only a small number of cases is expected.
2) Most AIDEP cases that are not upgraded to ductal carcinoma in situ (DCIS) are regarded as ADH on excision.

Lobular Lesions
Lobular neoplasia (LN) and lobular in situ neoplasia (LISN) are interchangeable terms and encompass the spectrum of lobular lesions from atypical lobular hyperplasia (ALH) through to lobular carcinoma in situ (LCIS).
In the UK, guidance is not to record a specific diagnosis of ALH or LCIS on core biopsy or diagnostic VAB because insufficient amounts of material are received to make the distinction with accuracy.Instead, the broader diagnosis of LISN is preferred.This view is reflected in the Sloane pathology data collection form from 2016 onwards.
However, this guidance is not followed consistently, and some pathologists will categorise a lesion as LCIS if they consider there are sufficient changes to make this diagnosis on a core biopsy specimen.
Others will (according to the guidelines) classify both ALH and LCIS under the umbrella term LISN, even if there are sufficient features for the atypia to be regarded as LCIS.The term ALH is not used in standard core or VAB reporting, as pathologists classify these as LISN.Thus, whilst the diagnosis of LISN on core biopsy or VAB will include a mixture of cases of ALH and LCIS, if the pathologist has classified the disease as LCIS this is accepted as reliable.
In an excision specimen pathologists will (almost always be able to) distinguish ALH from LCIS, thus ALH diagnoses was derived only from excision specimens.
An LCIS diagnosis may be derived from first diagnostic procedure (if the pathologist has diagnosed it as such) or excision.As a result, LCIS diagnoses were based on a mix of procedures, both core biopsy or VAB or excision specimens, although it is anticipated that the majority were derived from the latter.

Mixed Lesions
Women with atypia can have more than one type of atypia recorded: (a) because types of atypia not infrequently co-exist and may be present in any one specimen, or (b) because different specimens during investigation may result in different diagnoses because some specific diagnoses can only be made on larger volume samples.For the latter reason, the initial diagnoses may, therefore, be revised or specified on excision.Potentially, the initial sampling may remove substantial parts of the lesion resulting in a different diagnosis on subsequent excision.
We used the following three criteria when more than one atypia type was recorded to assign an atypia type for analysis.

Collection and definition of cancer events
Cancer events were directly reported by the screening units by retrospective review of a list of atypia patients sent to the centres by the Sloane project up until 2013.After that annual NCRAS extracts were used to link to the Cancer registration data to identify cancer events.Centres were asked to complete a Sloane recurrence form for each event.
Cancer events were ipsilateral breast/nodal or contralateral breast/nodal DCIS or invasive events six months or more after surgical or diagnostic events due to atypia diagnosis.
For women with more than one subsequent cancer event recorded only one event was included in the analysis of cancer rates.In general, the worse diagnosis was considered (i.e.invasive rather than DCIS).In cases with a contralateral and ipsilateral diagnosis on the same date, the contralateral diagnosis was included.In cases with a contralateral and ipsilateral diagnosis on two different dates the earlier diagnosis was included.Women with a DCIS diagnosis followed by invasive cancer diagnosis were handled differently for the primary (outcome is invasive cancer only) and secondary (outcome is the first event of either DCIS or invasive cancer) analysis.

Definition of cause of death 'breast cancer'
Cause of death data were recorded in the Sloane dataset using rules by the Office for National Statistics that apply the condition or conditions entered in the lowest completed line of Part I of the Medical Certificate Cause of Death (MCCD).For this analysis, a breast cancer death was required to have a record of cause of death 'breast cancer' and a record of a breast cancer event.A cancer event classified 'distant' in conjunction with a breast cancer death was analysed as 'other cancer'.

Additional analyses a)
Investigation of cancer diagnosis by age at atypia diagnosis We described the rates at all three time points (1 year, 3 years and 6 years) by age at atypia diagnosis to see if there are any indications of different results for women of different ages.

b) Investigation of temporal effects
The dataset contains women diagnosed with atypia from April 2003 to December 2018.Therefore, there might be some bias due to temporal effects (changes in screening technology, changes in terminology, additional atypia types, changes in diagnostic procedures, treatment and monitoring) resulting in changes in prognosis within any of the atypia types.We investigated temporal effects descriptively by looking at cancer rates for women diagnosed with atypia in different time cohorts of three 5-year periods (using date of atypia diagnosis) and comparing cancer rates and types of cancer detected at the beginning with the end of the cohort.

c) Consecutive cases only
There is some risk of selection bias if clinicians report only more interesting cases of atypia which would preclude generalisability of findings.We explored the data in 5-year intervals comparing atypia type and cancer events from consecutive cases and all cases.The definition for completeness to identify consecutive cases was based on two separate audit events: A request was sent out to five units on 8 th February 2017 to do a retrospective audit looking at patients from 2003-2006.All five units returned data for those years.Therefore, all patients with atypia from these units with a screening diagnosis from 01/04/2003 to 31/03/2006 would be classed as "Unit complete".
In 2019 a list of patients diagnosed with atypia from 01/04/2014 to 31/03/2017 was sent out to all units based on the B3 Crystal Report used by the Association of Breast Surgery (ABS)/NHSBSP Breast Screening Audit with a covering letter asking for completion of the atypia form for each patient as well as identifying who was ineligible.All patients with atypia with a screening diagnosis 01/04/2014 to 31/03/2017 from units who have returned all of their data forms were classed as "Unit complete".
In addition, two breast units sent batches of atypia forms for all years and one unit sent atypia for the majority of years.There are emails to confirm this and patients from the units in question were also assigned the category "Unit complete".

d) Investigation of impact of management strategy
Different levels of investigation (e.g.diagnosis by core biopsy only, VAB or surgical procedure) may have an impact on cancer prognosis.We explored cancer rates following diagnosis of atypia by three levels of management.
The categories of different management levels reflect the size of the sample taken for diagnostic purposes based on the following rationale: The diagnostic pathway for atypia following a recall from screening typically includes an initial diagnostic procedure (standard core or vacuum assisted biopsy (VAB)), followed by a second diagnostic procedure either surgical excision or a second vacuum assisted specimen.The initial procedure is diagnostic, while the second procedure includes a greater proportion of the lesion for diagnostic purposes and may even excise the whole area.This second VAB is, therefore, referred to as vacuum assisted excision (VAE).A VAE typically includes a larger sample than VAB and a surgical procedure often samples the largest volume.Women with an atypia diagnosis will have had different numbers and types of procedures undertaken along their diagnostic pathway based on the year of their diagnosis (i.e.before or after UK guidelines were published for management of B3 lesions (Pinder et al. 2018)), preference, availability of methods and whether they could technically be sampled by VAE.
The procedures vary in their diagnostic accuracy (because of the difference in amount of tissue received by the pathologist) as well as, potentially, their prognostic ability (as the recurrence rate may be affected by the amount of tissue sampled, and thus the extent of the area of atypia removed, during the investigation process).The three management strategies include women with: 1.
Only one initial diagnostic procedure (standard core or VAB); 2.
An initial diagnostic procedure (standard core or VAB) and a second vacuum assisted procedure (recorded as a (therapeutic) VAB or VAE) (no surgical procedure); 3.
An initial diagnostic procedure (standard core or VAB) and a minimum of one surgical procedure (+/-additional VAB/VAE).

Reference:
Pinder SE, Shaaban A, Deb R, Desai A, Gandhi A, Lee AHS, Pain S, Wilkinson L, Sharma N. NHS Breast Screening multidisciplinary working group guidelines for the diagnosis and management of breast lesions of uncertain malignant potential on core biopsy (B3 lesions).Clin Radiol. 2018;73(8) We analysed time-to-event data with competing risks, considering the first event that happened to each person only; In the main model the competing causes were a diagnosis of invasive breast cancer or death.We also modelled with invasive cancer split into ipsilateral and contralateral, and with invasive cancer and DCIS combined.There are two methods that are used: cause-specific hazards and subdistribution hazards.Putter et al. (2020) gives a succinct summary of the two methods.The cause-specific hazards method models the hazard over time for each cause separately, estimating the hazard of someone who has not yet had an event having an event of that cause.The subdistribution hazards method (Fine and Gray, 1999) evaluates the hazard of an event of the cause of interest amongst those who have yet to have an event of the cause of interest; which it does by including those who have had an event of another cause in with those who have yet to have an event in the "risk set", even though they are unable to have an event of the cause of interest at that time.
The purpose of the analysis may affect the choice of method.Lau et al. (2009) suggest that when looking at the "etiology" of the different causes then cause-specific hazards are better, whereas if trying to predict someone's risk then subdistribution hazards are preferred.We chose to use the cause-specific hazards method, because we wanted to explore how each cause affects the time to event specifically.However, we also decided to run the chosen model in the subdistribution framework, since prediction of a person's risk of an event is an important result for the study.We present the cumulative incidence function from the main model (evaluated at 1 year, 3 years and 6 years after atypia diagnosis) fitted with both cause-specific hazards and subdistribution hazards.
We chose to use the method of Hinchliffe and Lambert (2013b) for our main analysis, which enables us to model a baseline hazard function for each cause separately, each with their own shape.This involves using a dataset where each case is included once for each cause (twice for models with death and invasive cancer and three times when cancer is split into ipsilateral and contralateral).The method was run in Stata using the stpm2 package (Lambert and Royston, 2009).Variables indicating which cause the row relates to were added as main effects and time varying effects with 3 degrees of freedom so that the flexible parametric model can model the baseline hazard for each cause with a restricted cubic spline.The potential explanatory effects were added as main effects on the log cumulative hazard scale, by using scale(hazard) in the stpm2 command.Using the log cumulative hazard scale implies that the variables are being added into the model using proportional hazards.
We plotted Kaplan-Meier survival curves for the event death or invasive cancer (combining the two causes into one) stratified by the groups of each explanatory variable in the chosen model, and found nothing to suggest that assuming proportional hazards was unreasonable.The cause-specific cumulative incidence functions for various values of the explanatory variables in the model were calculated from the model using the postestimation command stpm2cif (Hinchliffe and Lambert, 2013a).The command produces estimates and confidence intervals for the cause-specific cumulative incidence function and the cause-specific hazard function.
A subdistribution hazards model was run using the same covariates as that of the chosen causespecific hazards model.The model was run in R using the Survival package; using the finegray function to adapt the dataset so that a Fine-Gray model can be run using the coxph function.This used invasive cancer as the event of interest and death as the competing risk, remaining in the "risk set".The cumulative incidence function for invasive cancer was estimated using the survfit function, using the "log-log" type of confidence intervals.
Interpretation of model coefficients in competing risks models.
Austin and Fine (2017) remind that in a competing risks model using cause-specific hazards the model coefficients only tell us about the effect of the explanatory variable on the cause-specific hazard function, and not about its effect on the cumulative incidence function.This is because the cumulative incidence function for each cause is dependent on the cause-specific hazard functions for all causes, not just its own cause.Therefore, care must be taken in interpretating the cause-specific hazards model coefficients.The cause-specific hazard ratios we give show the effect of the explanatory variable on that cause's cause-specific hazard function only.
We have presented the cause-specific cumulative incidence functions for each cause, evaluated at given time points for combinations of values for the three explanatory variables in the model.The effect of each explanatory variable on the cumulative incidence functions can be seen by considering the evaluations in these tables.
Austin and Fine also state that for the subdistribution hazard model the hazard ratio does not show the size of the effect of the explanatory variable on the cumulative incidence function, like it does on the subdistribution hazard function.
Therefore, we advise care when evaluating model coefficients and hazard ratios from competing risks models.For cause-specific hazards models in particular there is no single number that can be used to evaluate the effect of an explanatory variable on the cumulative incidence function.
Jason P Fine and Robert J Gray (1999) "A proportional hazards model for the subdistribution of a competing risk."Journal of the American Statistical Association, 94:496-509.

Study population
Figure S1 Flow diagram of study population         Table S9 Invasive cancer rates at 3 years post atypia by atypia type estimated from CIF ADH FEA LISN Mixed Invasive cancer per 1000 women with atypia (95% CI) 13.6 (8.1,21.6)9.47 (2.62,25.9) 14.3 (8.23,23.5) 20.8 (9.21,40.6)Table S10 Invasive cancer rates at 1 year, 3 years and 6 years post atypia diagnosis per 1000 women estimated from CIF for 3 time periods under different scenarios The CIF for the causes in the main model (invasive cancer and death) are evaluated at 1, 3 and 6 years in table S14.For someone aged 60 with high background parenchymal density the estimated rate of invasive cancer at 3 years for those diagnosed with atypia between 2003 and 2007 was 38.95 per 1000 women, 95% CI (23.14,49.54),and for those diagnosed with atypia between 2013 and 2018 was 10.31 per 1000 women, 95% CI (4.93,15.70).For low background parenchymal density, the corresponding rates were 29.72, (18.20,41.23), and 7.84, (3.99,11.69).The hazard ratios presented in Table S15 come from different models.Age, density and year of diagnosis was compared in the main model.Other variables were added in turn to the main model to derive hazard ratios for the relevant comparisons.21.34 (3.78,38.9) 49.5 (9.11,89.9)*The assumptions used to calculate the confidence intervals can occasionally lead to the lower bound taking a small negative value.These are given as zero in the table

Figure
Figure S5 Number of cancers (invasive and DCIS) over time since atypia diagnosis by mode of detection diagnosis with atypia; by age at diagnosis, year of diagnosis and background parenchymal density (high in figure a, low in figure b) 1. METHODS 1.1 Variables received from the Sloane Project data in accordance with the inclusion and exclusion criteria as per the Office for Data Release data sharing contract diagnostic/therapeutic pathology Adjuvant treatment (i.e.radiotherapy, endocrine therapy, none) Sally R Hinchliffe and Paul C Lambert (2013a) "Extending the flexible parametric survival model for competing risks" The Stata Journal 13, Number 2, pp.344-355 Sally R Hinchliffe and Paul C Lambert (2013b) "Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions" BMC Medical Research Methodology 2013, 13:13, http://www.biomedcentral.com/1471-2288/13/13Paul C. Lambert and Patrick Royston (2009) "Further development of flexible parametric models for survival analysis" The Stata Journal, 9:2, p 265-290.

Figure
Figure S3 Proportion of diagnostic management options performed by year

Figure S4 .
Figure S4.Cumulative incidence function for invasive cancer and death from the main model, evaluated at 1 year, 3 years, and 6 years since diagnosis with atypia; by age at diagnosis, year of diagnosis and women's breast background parenchymal density (high figure 3a, low figure 3b). a High density

Figure
Figure S6 Cause-specific hazard function for each cause of outcome from the main model by time since atypia diagnosis.Shown by age at diagnosis, year of diagnosis and background parenchymal density (high in figure a and low in figure b) a High density

Figure
Figure S9 Cumulative incidence functions evaluated at 1 year, 3 years, and 6 years since diagnosis with atypia, from the main model with invasive cancer or DCIS combined for high (a) and low (b) background parenchymal density

Figure
Figure S10 Cumulative incidence function for invasive cancer from the main model using the subdistribution method, evaluated at 1 year, 3 years, and 6 years since diagnosis with atypia; by age at diagnosis, year of diagnosis and background parenchymal density (high in figure a, low in figure b) a High density

Content 1 .
Methods 1.1.Variables received from the Sloane Project data in accordance with the inclusion and exclusion criteria as per the Office for Data Release data sharing contract 1.2.Atypia types 1.3.Collection and definition of cancer events 1.4.Definition of cause of death 'breast cancer' 1.5.Additional analyses 1.6.Options explored how to add age at diagnosis into the model 1.7.Flexible parametric model choice -rationale Cancers typically take longer than 12 months to develop.Cancer diagnoses within 12 months after the atypia diagnosis likely represent missed cancers due to under sampling.We performed an analysis to explore the impact of excluding cancers diagnosed within one year of a diagnosis from the main analysis.1.6Options explored how to add age at diagnosis into the model Four methods of including age at diagnosis were explored: grouped (46 to 55, 56 to 60, 61 to 65, 66 to 70, 71 to 95), continuous linear, continuous linear and quadratic, and a cubic spline.Including age as a continuous, linear term was the best method, having better model fit statistics than the equivalent models for group, linear and quadratic and spline, showing the extra complexity to be unnecessary.
1.7 Flexible parametric model choice -rationale Table S1 Descriptive statistics of all women with atypia for the study period April 2003 to June 2018

Table S2 Number
of women with atypia and characteristics of subsequent cancers detected following atypia diagnosis by atypia type for three time periods separately TableS3Characteristics of subsequent invasive and non-invasive cancers by atypia type and number of deaths An additional 2 women had a recorded distant cancer, but no breast cancer recorded.** This includes one woman with an invasive cancer recorded but no date of detection, who is therefore not included in the analysis of cancer rates at 1, 3, and 6 years following atypia diagnosis.†This includes 326 (10.1%) women who received an AIDEP diagnosis without an ADH diagnosis ‡No death certificate only breast cancer death occurred, however, one additional woman with a distant cancer but no record of a breast cancer had breast cancer as cause of death *

Table S6
Invasive cancers per 1000 women with atypia at 1 year and 3 years post atypia diagnosis estimated from CIF by management strategy and site of invasive cancers separately for atypia types TableS8Invasive cancer rates per 1000 women with atypia at 1 year, 3 years and 6 years post atypia diagnosis by age group estimated from CIF

Mode of detection of subsequent cancers
Figure S5 Number of cancers (invasive and DCIS) over time since atypia diagnosis by mode of detection density) is the best model.We chose to use the age, year and background parenchymal density model since it is the best according to AIC, the descriptive statistics show that year of diagnosis was important, and clinical opinion that background parenchymal density is important.Adding type of atypia, management, and calcification to the age and year model did not improve the model fit statistics.Adding a variable of consecutive versus non-consecutive cases did not improve the model fit.Including age as a continuous, linear term was the best method, having better model fit statistics than the equivalent models for group, linear and quadratic and spline, showing the extra complexity to be unnecessary.

Table S15
Comparison of the risk of subsequent invasive cancers considering different factors Age is a continuous variable measured in years, so the change is over a period of one year Table S16 Fitted values at 1, 3, and 6 years since atypia diagnosis from main model with invasive cancer and DCIS combined.Cumulative incidence of outcome cause per 1000 women.
TableS17Cancer rates (fitted values) at 1, 3, and 6 years since atypia diagnosis from main model, using the subdistribution method.Cumulative incidence of invasive cancer per 1000 women